The city of Seattle is known as a busy city. The population has been increasing, and new buildings are being constructed and old buildings are being demolished. If you want to see if the city of Seattle is really being developed, you need to know numbers: population, number of buildings being constructed or demolished, the purpose of buildings being constructed or demolished. Using the package ggmap, we will explore the dataset of demography in the state of Washington and building information in the city of Seattle.
## 'data.frame': 39 obs. of 3 variables:
## $ subregion: Factor w/ 39 levels "Adams","Asotin",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ area : Factor w/ 39 levels "1,140.10","1,242.20",..: 15 34 7 27 9 33 37 1 12 21 ...
## $ density : Factor w/ 39 levels "1,018.02","10.32",..: 2 18 5 13 26 36 25 38 11 16 ...
## 'data.frame': 39 obs. of 3 variables:
## $ subregion: chr "adams" "asotin" "benton" "chelan" ...
## $ area : chr "1,925.00" "636.2" "1,700.40" "2,920.50" ...
## $ density : chr "10.32" "35.04" "113.8" "26.31" ...
Subregion is each subregion in the state of Washington, including the subregion of King, where the city of Seattle is located.
Area is the total land area of each subregion, in square mile in 2010.
Density is the population density in 2017.
Lastly I used Pandas in python to retrieve filtered information as described above.
You can look up the detail of dataset from the Office of Financial Management in Washington: http://www.ofm.wa.gov/pop/popden/\
This is the map of the United States. Since we need a map of state, not nationwide, we will get the map of Washington state only.
This is Washington state! Now we need to look up the counties (subregions).
This seems good, ready to plot on the map. Before doing that, we need to merge the dataset from Google Maps, which has the information of longitude, latitude, and subregion, and the dataset from the Office of Financial Management in Washington, by doing inner join with the common column of subregion. After that, there needs some data cleaning, and get the data of population by simply multiplying density * area.
Here is the map of population. As expected, the county of King has the highest population. We can see the density, too.
As expected, the county of King has the highest population density.
This one looks much better by giving some color.
Yes, highest population density. Unfortunately, there is no more detail more than a county, we cannot see the area, population, or population density of each districts or zip codes in the county of King.
## 'data.frame': 55842 obs. of 20 variables:
## $ Application.Permit.Number : int 6578806 6409682 6529715 6578702 6564726 6524389 6588198 6600440 6584946 6572385 ...
## $ Permit.Type : Factor w/ 3 levels "Construction",..: 1 3 1 1 1 1 1 1 1 1 ...
## $ Address : Factor w/ 33345 levels "","1 YESLER WAY",..: 27541 17054 33281 2784 23607 13530 4737 9797 13480 25690 ...
## $ Description : Factor w/ 48174 levels "","'Construct terraced garage with a DADU in the rear yard of existing SFR' Revised from-Upper floor addition a"| __truncated__,..: 18545 45062 41593 18677 33190 39359 20775 46107 18041 21093 ...
## $ Category : Factor w/ 6 levels "","COMMERCIAL",..: 6 1 6 6 6 6 6 2 6 2 ...
## $ Action.Type : Factor w/ 17 levels "","ADD/ALT","ALTER",..: 2 13 2 2 2 2 2 15 2 10 ...
## $ Work.Type : Factor w/ 2 levels "No plan review",..: 1 2 2 1 1 2 2 2 2 2 ...
## $ Value : Factor w/ 13309 levels "$0.00 ","$1,000,000.00 ",..: 10084 1 8620 12831 5199 1 8211 1 1800 1338 ...
## $ Applicant.Name : Factor w/ 15248 levels "","#1 LINDA SIAUW, #2 TAINE WILTON",..: 3174 379 10708 14216 13907 13816 10928 3940 2703 8213 ...
## $ Application.Date : Factor w/ 2488 levels "","1/10/2008",..: 145 145 1652 145 145 1 1783 1783 1783 708 ...
## $ Issue.Date : Factor w/ 1387 levels "","1/10/2013",..: 82 1 834 82 82 1 1 1 1 1 ...
## $ Final.Date : Factor w/ 1269 levels "","1/10/2013",..: 1 79 925 1 1 1 1 1 1 1 ...
## $ Expiration.Date : Factor w/ 1641 levels "","1/1/2015",..: 1332 1 334 1332 1332 1 1 1 1 1 ...
## $ Status : Factor w/ 13 levels "","AP Closed",..: 12 2 10 12 12 1 4 4 4 4 ...
## $ Contractor : Factor w/ 2245 levels "","1 BOND TOWER LLC, AMAZON.COM",..: 1 628 1 1 1 1 1 1 1 628 ...
## $ Permit.and.Complaint.Status.URL: Factor w/ 55837 levels "http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6061738",..: 50671 23287 42870 50659 48413 42055 52275 54351 51718 49674 ...
## $ Master.Use.Permit : int NA NA NA NA NA NA NA NA NA 3022541 ...
## $ Latitude : num 47.7 47.6 47.7 47.7 47.7 ...
## $ Longitude : num -122 -122 -122 -122 -122 ...
## $ Location : Factor w/ 33345 levels "","1 YESLER WAY\n(47.60157484, -122.33574983)",..: 27541 17054 33281 2784 23607 13530 4737 9797 13480 25690 ...
## left bottom right top
## -122.44950 47.47250 -122.21997 47.75774
## 'data.frame': 21439 obs. of 13 variables:
## $ Permit.Type : Factor w/ 3 levels "Construction",..: 1 1 1 1 1 1 1 1 2 1 ...
## $ Category : Factor w/ 6 levels "","COMMERCIAL",..: 6 6 6 6 2 6 2 6 6 6 ...
## $ Action.Type : Factor w/ 17 levels "","ADD/ALT","ALTER",..: 2 2 2 2 15 2 10 10 6 2 ...
## $ Work.Type : Factor w/ 2 levels "No plan review",..: 1 1 1 2 2 2 2 2 1 2 ...
## $ Value : num 50000 9500 280000 40000 0 ...
## $ Application.Date: Factor w/ 2488 levels "","1/10/2008",..: 145 145 145 1783 1783 1783 708 1618 145 1644 ...
## $ Issue.Date : Factor w/ 1387 levels "","1/10/2013",..: 82 82 82 1 1 1 1 301 1 983 ...
## $ Final.Date : Factor w/ 1269 levels "","1/10/2013",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Expiration.Date : Factor w/ 1641 levels "","1/1/2015",..: 1332 1332 1332 1 1 1 1 390 1 503 ...
## $ Status : Factor w/ 13 levels "","AP Closed",..: 12 12 12 4 4 4 4 12 13 12 ...
## $ Contractor : Factor w/ 2245 levels "","1 BOND TOWER LLC, AMAZON.COM",..: 1 1 1 1 1 1 628 1666 1 1 ...
## $ Latitude : num 47.7 47.7 47.7 47.7 47.6 ...
## $ Longitude : num -122 -122 -122 -122 -122 ...
Dataset has the list of buildings permitted by the city of Seattle with 55900 rows and 20 variables. For each building, it has its unique application permit number with other characteristics described by variables. It is refreshed daily. More details are available from: https://data.seattle.gov/Permitting/Building-Permits-Current/mags-97de
This is the map of the city of Seattle.
In order to see if the city of Seattle is really being developed, we need to know how many buildings are permitted under construction, demolition, or site development.
We can see the number of permits under construction is significantly more than demolition, so the city of Seattle is actively being developed as of now.
There are many building being used as commercial in downtown. we can also see many institutional buildings around the center and north of the city of Washington, where actually University of Washington and Seattle Central College are located. There are two areas that do not have many building in northwest and southwest, because there is a park and a harbor in each. we can also see many industrial building in the south, and that’s because there is an industrial district.
This one does not show anything because Value is too inconsistent, needs a transformation by applying log10.
We can see that many buildings in the City of Seattle are valued in between $100,000 - $1,000,000, which is not relatively low nor relatively high compared to other cities. Note that most of expensive buildings are in the center and north of the city of Seattle, where the downtown and so-called “rich area” are located.
By seeing the map we can see what and how the city of Seattle is being developed.
Yet, it is hard to make sure, because they are mixed up: industrial buildings are obviously more expensive than single house, and the buildings being demolished are surely under-appreciated or have no value at all. The plots following will see the details of each.
Although many commercial constructions are adding or alternating their building, there are still many new buildings, even in downtown, where the price is highest. Most industrial constructions, on the other hand, are mostly just adding or alternating. For the action type of multifamily, newly constructing buildings are dominating. Constructions of single family are similar to ones of commercial, but the area they have is different. While I do not see many constructions of institutional, I see most of buildings are pointing University of Washington or Seattle Central College.
I see that regardless of category of the buildings, there is very few deconstruction or relocation. Also I see that, compared the area that the constructions of multifamily are being built, there are not many demolition of multifamily in downtown. I can predict that there are some constructions of tower blocks, because it is almost impossible to have such high constructions with low demolition, as the area is very limited.
Most notable thing from these are, there are still many buildings that are being graded.
I was surprised how low density some county have. I don’t see any particularly large nor small county, but the county of King has more than 1000 density, and some other counties have lower than 4 density. However, I can carefully predict something from here that most of people in the city of Seattle are from other county or state, as its population density imbalance is abnormal.
I could somehow predict that the city of Seattle is actively being developed even before I started analysis on it, but didn’t know there are that many constructions now. If I see only the number of constructions, and compare it with demolition, I would be safe to say that the city of Seattle is new born city.
A lot of multifamily (I assume it is a tower block as its density is extremely high) are adding, alternating, or newly built in downtown and in coastal line. I was expecting, just like downtown in other city, more buildings of commercial and less buildings of multifamily being constructed in downtown.
First of all, I was able to get and use the map data, there was no data about district, which would contain a lot more detail about the city of Seattle. If there was, I would be able to get a lot better and accurate visualized plots.
Second, I could get a statistics so that could get a sense if, what, and how the city of Seattle is being developed, but nothing beyond it. If I had more sense of analyzing data, I would be able to get something other than just statistics itself, but I was not able to.
At last, although yet I could get meaningful statistics, there are more than half of buildings that are not valid anymore (application closed, permit closed, or just cancelled). I would be able to get better analysis if all of data in the dataset was valid.
I used three datasets: one about general demography from ggmap, one about specific demography in the state of Washington from the Office of Financial Management in Washington, and last one from the city of Seattle. I explored the dataset and analyzed what category of constructions or demolitions are in progress in which area of the city of Seattle, and what the action type of that construction, destruction, or site development is.
The city of Seattle is actively being developed just like new born city, and it is not limited to only commercial, but also residential, institutional, and other purposes. Yet most of buildings, except in the area of downtown, are not as expensive as New York City or San Francisco Bay area.
If there was enough time, I would also compare those buildings by yearly, so can find out if this kind of construction boom is recent, or historically has been done like this.